In the last twenty years, the development of mobile telecommunications devices have has dramatically expanded and modified the ways in which people communicate. Computers with ever-faster computer processors enabled faster communication with increased processing speed and improved analysis of vast quantities of data. In addition, sensor technology has also rapidly expanded how patients have been monitored, even by non-professionals. The development of various sensors enabled a variety of measurements to be taken and analyzed by a computer to generate useful information. In recent years, the use of medical sensing technology in combination with various communications platforms has provided new and interesting ways for people, including patients, to be monitored or to monitor themselves and communicate the results of the monitoring with their physician or caregiver. For example, mobile devices such as smart phones have enabled mobile device users to communicate remotely and provided some ability to obtain, analyze, use, and control information and data. For example, a mobile device user may be able to use application software (an “app”) for various individualized tasks, such as recording their medical history in a defined format, playing a game, reading a book, etc. An app may work with a sensor in a mobile device to provide information that a user wants. For example, an app may work with an accelerometer in a smart phone and determine how far someone walked and how many calories were burned during the walk.
The use of a mobile communications platform such as a smartphone with one or more such biometric sensors has been described in various contexts. For example, U.S. Publication No. US2010/0029598 to Roschk et al. describes a “Device for Monitoring Physical Fitness” that is equipped with a heart rate monitor component for detecting heart rate data and an evaluation device for providing fitness information that can be displayed by a display device and is derived by a processing unit, embodied for reading in and including supplementary personal data. U.S. Publication No. US2009/0157327 to Nissila describes an “Electronic Device, Arrangement, and Method of Estimating Fluid Loss” that is equipped with “an electronic device comprising: a processing unit configured to receive skin temperature data generated by a measuring unit, to receive performance data from a measuring unit, and to determine a theoretical fluid loss value on the basis of the received performance data.”
Similarly, clothing that includes sensors have been previously suggested. See, e.g., U.S. Publication No. US2007/0178716 to Glaser et al., which describes a “modular microelectronic-system” designed for use with wearable electronics. U.S. Publication No. US2012/0071039 to Debock et al. describes interconnect and termination methodology fore-textiles that include a “conductive layer that includes conductors includes a terminal and a base separately provided from the terminal. The terminal has a mating end and a mounting end.” U.S. Publication No. US2005/0029680 to Jung et al. describes a method and apparatus for the integration of electronics in textiles.
For example, cardiovascular and other health-related problems, including respiratory problems may be detected by monitoring a patient. Monitoring may allow early and effective intervention, and medical assistance may be obtained based on monitored physiological characteristics before a particular health issue becomes fatal. Unfortunately, most currently available cardiovascular and other types of health monitoring systems are cumbersome and inconvenient (e.g., impractical for everyday use) and in particular, are difficult or impractical to use for long-term monitoring, particularly in an unobtrusive manner.
It has been proposed that patient health parameters, including vital signs (such as ECG, respiration, blood oxygenation, heart rate, etc.) could be actively monitoring using one or more wearable monitors, however, to date such monitors have proven difficult to use and relatively inaccurate. Ideally such monitors could be unobtrusively worn by the subject (e.g., as part of a garment, jewelry, or the like). Although such garments have been proposed, see, e.g., U.S. Publication No. 2012/0136231, these garments suffer from a number of deficits, including being uncomfortable, difficult to use, and providing inaccurate results. For example, in applications such as U.S. Publication No. 2012/0136231, a number of individual electrodes are positioned on the garment and connected to a processor by woven conductive fibers or the like; although such garments “require . . . consistent and firm conductive contact with the subject's skin,” in order to provide accurate readings, such designs require that the garment be restrictive in order to prevent movement of the garment (and thus sensors) contacting these skin regions. Such a configuration rapidly becomes uncomfortable, particularly in a garment that would ideally be worn for many hours or even days. In addition, even such tightly worn garments often move relative to the wearer (e.g., slip or ride up). Further, devices/garments such as those described in the prior art are difficult and expensive to manufacture, and are often rather “fragile”, preventing robust usage and washing. Finally, such devices/garments typically do not allow processing of manual user input directly on the garment, but either relay entirely on passive monitoring, or require an interface of some sort (including off-garment interfaces).
The use of garments including one or more sensors that may sense biometric data have not found widespread use. In part, this may be because such garments may be limited in the kinds and versatility of the inputs that they accept, as well as limits in the comfort, and form factor of the garment. For example, sensors, and the leads providing power to and receiving signals from the sensors have not been fully integrated with the garment in a way that allows the garment to be flexible, attractive, practical, and above all, comfortable. For example, most such proposed garments have not been sufficiently stretchable. Finally, such proposed garments are also limited in the kind of data that they can receive, and how they process the received information.
Thus, existing garments (e.g., devices and wearable sensing apparatuses) and processes for analyzing and communicating the physical and emotional status of an individual may be inaccurate, inadequate, limited in scope, unpleasant, and/or cumbersome.
It is beneficial to have wearable garments having one more sensors that may be comfortably worn, yet provide relatively accurate and movement-insensitive measurements over a sustained period of time.
The sensors integrated with the wearable garments, e.g., sensors implemented with MEMS technology, are generally affected by some noises that limit their performances. For most MEMS devices white noise (random walk noise) and uncorrected bias errors are the main sources of inaccuracy in their measurements. Specifically, white noise (random walk noise) and uncorrected bias errors are the main sources of inaccuracy in the integration of their measurements. Therefore, the sensors integrated with the wearable garments have to be calibrated for accurate measurements.
In addition, in the field of wearable garments, the issues of inaccuracy in measurements are enhanced by the presence of multiple non-rigidly connected sensors. Furthermore, the requirement to keep the garment integrated smartphone away from the sensors because it changes the magnetic environment adds more difficulty to the calibration process. All these requirements complicated the calibration process.
Conventionally, the calibration routine is based on a sequential calibration of each sensor. During this routine, the user has to manually interact with each sensor in order to obtain the calibration parameters. This conventional process can be very long for a wearable garment with several sensors.
There is a need to develop a calibration box for the wearable garment that is able to calibrate the multiple sensors on the wearable garment simultaneously and address the specific challenges of the calibration process for garment integrated sensors.